Technologies for dynamically sharing remote resources across remote computing nodes

Abstract

Technologies for dynamically sharing remote resources include a computing node that sends a resource request for remote resources to a remote computing node in response to a determination that additional resources are required by the computing node. The computing node configures a mapping of a local address space of the computing node to the remote resources of the remote computing node in response to sending the resource request. In response to generating an access to the local address, the computing node identifies the remote computing node based on the local address with the mapping of the local address space to the remote resources of the remote computing node and performs a resource access operation with the remote computing node over a network fabric. The remote computing node may be identified with system address decoders of a caching agent and a host fabric interface. Other embodiments are described and claimed.

What is claimed is: 
     
         1 . A server system for use in network communication with at least one remote computer node via at least one network, the server system comprising:
 network communication circuitry for use in the network communication with the at least one remote computer network via the at least one network; and   processing circuitry configurable to perform operations comprising:
 based upon machine learning and request data, dynamically assigning one or more resources for use in service request processing associated with the at least one remote computer node, the request data to be based upon one or more requests to be provided from the at least one remote computer node via the at least one network; 
   wherein:
 the service request processing is configurable to comprise:
 accessing the one or more resources in accordance with mapping data for use in identifying the one or more resources in association with the service request processing; and/or 
 providing to the at least one remote computer node, via the at least one network, other data in response, at least in part, to the one or more requests; and 
 
 the machine learning is configurable to be based upon one or more of:
 resource usage monitoring data; 
 resource demand history data; 
 dynamic condition data; and/or 
 application requirement data. 
 
   
     
     
         2 . The server system of  claim 1 , wherein:
 the one or more resources comprise a plurality of resources that are comprised in multiple computer nodes; and   the machine learning is configurable to be based upon:
 service level data; and/or 
 policy data. 
   
     
     
         3 . The server system of  claim 1 , wherein:
 the operations further comprise:
 after the dynamically assigning of the one or more resources:
 releasing, based upon the machine learning, assignment of the one or more resources. 
 
   
     
     
         4 . The server system of  claim 1 , wherein:
 after the dynamically assigning of the one or more resources:
 the mapping data is to be reconfigured to reflect releasing of assignment of the one or more resources. 
   
     
     
         5 . The server system of  claim 1 , wherein:
 the mapping data comprises address mapping data.   
     
     
         6 . At least one non-transitory machine-readable storage medium storing instructions for being executed by processing circuitry of a server system, the server system to be used in network communication with at least one remote computer node via at least one network, the instructions, when executed by the processing circuitry resulting in the server system being configured for performance of operations comprising:
 based upon machine learning and request data, dynamically assigning one or more resources for use in service request processing associated with the at least one remote computer node, the request data to be based upon one or more requests to be provided from the at least one remote computer node via the at least one network;   wherein:
 the service request processing is configurable to comprise:
 accessing the one or more resources in accordance with mapping data for use in identifying the one or more resources in association with the service request processing; and/or 
 providing to the at least one remote computer node, via the at least one network, other data in response, at least in part, to the one or more requests; and 
 
 the machine learning is configurable to be based upon one or more of:
 resource usage monitoring data; 
 resource demand history data; 
 dynamic condition data; and/or 
 application requirement data. 
 
   
     
     
         7 . The at least one non-transitory machine-readable storage medium of  claim 6 , wherein:
 the one or more resources comprise a plurality of resources that are comprised in multiple computer nodes; and   the machine learning is configurable to be based upon:
 service level data; and/or 
 policy data. 
   
     
     
         8 . The at least one non-transitory machine-readable storage medium of  claim 6 , wherein:
 the operations further comprise:
 after the dynamically assigning of the one or more resources:
 releasing, based upon the machine learning, assignment of the one or more resources. 
 
   
     
     
         9 . The at least one non-transitory machine-readable storage medium of  claim 6 , wherein:
 after the dynamically assigning of the one or more resources:
 the mapping data is to be reconfigured to reflect releasing of assignment of the one or more resources. 
   
     
     
         10 . The at least one non-transitory machine-readable storage medium of  claim 6 , wherein:
 the mapping data comprises address mapping data.   
     
     
         11 . A method implemented using a server system, the server system to be used in network communication with at least one remote computer node via at least one network, the method comprising:
 based upon machine learning and request data, dynamically assigning one or more resources for use in service request processing associated with the at least one remote computer node, the request data to be based upon one or more requests to be provided from the at least one remote computer node via the at least one network;   wherein:
 the service request processing is configurable to comprise:
 accessing the one or more resources in accordance with mapping data for use in identifying the one or more resources in association with the service request processing; and/or 
 providing to the at least one remote computer node, via the at least one network, other data in response, at least in part, to the one or more requests; and 
 
 the machine learning is configurable to be based upon one or more of:
 resource usage monitoring data; 
 resource demand history data; 
 dynamic condition data; and/or 
 application requirement data. 
 
   
     
     
         12 . The method of  claim 11 , wherein:
 the one or more resources comprise a plurality of resources that are comprised in multiple computer nodes; and   the machine learning is configurable to be based upon:
 service level data; and/or 
 policy data. 
   
     
     
         13 . The method of  claim 11 , wherein:
 the operations further comprise:
 after the dynamically assigning of the one or more resources:
 releasing, based upon the machine learning, assignment of the one or more resources. 
 
   
     
     
         14 . The method of  claim 11 , wherein:
 after the dynamically assigning of the one or more resources:
 the mapping data is to be reconfigured to reflect releasing of assignment of the one or more resources. 
   
     
     
         15 . The method of  claim 11 , wherein:
 the mapping data comprises address mapping data.